library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)

# For ts stuff: 
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)

# For spatial stuff: 
library(sf)
library(tmap)
library(mapview)
us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>% 
  clean_names()
renew_clean <- us_renew %>% 
  mutate(description = str_to_lower(description)) %>% 
  filter(str_detect(description, pattern = "consumption")) %>% 
  filter(!str_detect(description, pattern = "total")) # gets rid of options with word total

convert yyyymm column to date

renew_date <- renew_clean %>% 
  mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>% 
  mutate(month_sep = yearmonth(yr_mo_day)) %>% 
  mutate(value = as.numeric(value)) %>% 
  drop_na(month_sep, value)



# Make a version where i have the month and year in separate columns

renew_parsed <- renew_date %>% 
  mutate(month = month(yr_mo_day), label = TRUE) %>% 
  mutate(year = year(yr_mo_day))

Look at it

renew_gg <- ggplot(data = renew_date, aes(x = month_sep, 
                                          y = value,
                                          group = description)) + 
  geom_line(aes(color = description))

renew_gg

Updating colors with paletter pallettes

renew_gg +
  scale_color_paletteer_d("palettetown::bellsprout")

Coerce renew_parsed to a tsibble

renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)
renew_ts %>%  autoplot(value)

renew_ts %>% gg_subseries(value)

# renew_ts %>% gg_season(value)

ggplot(data = renew_parsed, aes(x = month, 
                                y = value,
                                group = year)) +
  geom_line(aes(color = year)) +
  facet_wrap(~description,
             ncol = 1,
             scales = "free",
             strip.position = "right")

just look at the hydroelectric energy consumption

hydro_ts <- renew_ts %>% 
  filter(description == "hydroelectric power consumption")

hydro_ts %>% 
  autoplot(value)

hydro_ts %>% 
  gg_subseries(value)

# hydro_ts %>%  gg_season(value)

ggplot(hydro_ts, aes(x = month,
                     y = value,
                     group = year)) +
  geom_line(aes(color = year))

What if i want quarterly average consumption for hydro?

hydro_quarterly <- hydro_ts %>% 
  index_by(year_qu = ~(yearquarter(.))) %>% 
  summarize(avg_consumption = mean(value))

head(hydro_quarterly)
## # A tsibble: 6 x 2 [1Q]
##   year_qu avg_consumption
##     <qtr>           <dbl>
## 1 1973 Q1            261.
## 2 1973 Q2            255.
## 3 1973 Q3            212.
## 4 1973 Q4            225.
## 5 1974 Q1            292.
## 6 1974 Q2            290.

Decompose that hydro_ts

dcmp <- hydro_ts %>% 
  model(STL(value ~ season(window = 5)))

components(dcmp) %>% autoplot()

hist(components(dcmp)$remainder)

Now look at the ACF

hydro_ts %>% 
  ACF(value) %>% 
  autoplot()

# DANGER

hydro_model <- hydro_ts %>% 
  model(
    ARIMA(value)
  ) %>% 
  fabletools::forecast(h = "4 years"
                       )

hydro_model %>% autoplot(filter(hydro_ts, year(month_sep) > 2010))

MAke a world map

world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
                 layer = "TM_WORLD_BORDERS_SIMPL-0.3")
mapview(world)